Emotion recognition and evaluation from mandarin speech signals

International Journal of Innovative Computing, Information and Control

Volumn 4, Issue 7, 2008, Pages 1695-1709

  Pao, Tsanglong ^a   Chen, Yute ^a   Yeh, Junheng ^a  

^a TATUNG UNIVERSITY   (Taiwan)

Author keywords

Emotional speech recognition and evaluation; McNemar's test; Radar chart; Weighted D KNN classifier

Indexed keywords

EID: 63649117187     PISSN: 13494198     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (28)

1. A. H. Cicca, M. Step and L. Turkstra, Show me what you mean: Nonverbal communication theory and application, The ASHA Leader, vol.34, pp.4-5, 2003.
2. R. Picard, Affective Computing, Cambridge, MA: MIT Press, 1997.
3. The Speech-Language-Hearing Association of The Republic of China, http://www.slh.org.tw/
4. Chinese National Association of the Deaf, Taiwan, R.O.C, http://www.cnad.org.tw
5. F. Jeffery, J. F. Cohn and G. S. Katz, Bimodal expression of emotion by face and voice, The Sixth ACM International Multimedia Conference, Bristol, England, pp.41-44, 1998.
6. P. Ekman and E. Rosenberg, What the Face Reveals, NY: Oxford University. 1997.
7. M. T. Lin, Yllable Recognition for Mandarin Chinese - A Hybrid Approach, PhD Dissertation, Tatung University, Taipei, Taiwan, 2003.
8. S. J. Mozziconacci and D. J. Hermes, Role of intonation patterns in conveying emotion in speech, Proc. of the 14th International Congress of Phonetic Sciences, San Francisco, USA, pp.2001-2004, 1999.
9. C. E. Williams and K. N. Stevens, Emotions and speech: Some acoustical factors, Journal of the Acoustical Society of America, vol.52, pp.1238-1250, 1972.
10. R. Murray and J. L. Arnott, Toward the simulation of emotion in synthetic speech: A review of the literature of human vocal emotion, Journal of the Acoustic Society of America, vol.93, pp.1097-1108, 1993.
11. K. R. Scherer, R. Banse and H. G. Walbott, Emotion inferences from vocal expression correlate across languages and cultures, Journal of Cross-Cultural Psychology, vol.32, pp.76-92, 2001.
12. T. S. Polzin, Detecting Verbal and Non-verbal Cues in the Communication of Emotions, PhD Dissertation, Carnegie Mellon University, 1999.
13. V. A. Petrushin, Emotion in speech: Recognition and application to call centers, Proc. of the Artificial Neural Networks in Engineering, pp.7-10, 1999.
14. S. McGilloway, R. Cowie, E. Doulas-Cowie, S. Gielen, M. Westerdijk and S. Stroeve, Approaching automatic recognition of emotion from voice: A rough benchmark, Proc. of the ISCA Workshop on Speech and Emotion, Belfast, pp.200-205, 2000.
15. N. Amir, Classifying emotions in speech: A comparison of methods, Proc. of the Eurospeech, Aalborg, Denmark, pp.127-130, 2001.
16. C. M. Lee and S. Narayanan, Towards detecting emotions in spoken dialogs, IEEE Trans. Speech and Audio Processing, vol.13, no.2, pp.293-302, 2004.
17. L. T. Bosch, Emotions, speech and the ASR framework, Speech Communication, vol.40, pp.213-215, 2003.
18. Y. L. Lin and G. Wei, Speech emotion recognition based on HMM and SVM, Proc. of the 4th International Conference on Machine Learning and Cybernetics, Guangzhou, pp.18-21, 2005.
19. S. Mitsuyoshi, F. Ren, Y. Tanaka and S. Kuroiwa, Non-verbal voice emotion analysis system, International Journal of Innovative Computing, Information and Control, vol.2, no.4, pp.819-830, 2006.
20. I. S. Engberg and A. V. Hansen, Documentation of the danish emotional speech database, Dept. Comm. Tech. Inst. Electron. Sys., Aalborg University, 1996.
21. C. E. Osgood, J. G. Suci and P. H. Tannenbaum, The Measurement of Meaning, The University of Illinois Press, Urbana, 1957.
22. F. V. Nelwamondo, T. Marwala and U. Mahola, Early classifications of bearing faults using hidden Markov models, gaussian mixture models, mel-frequency cepstral coefficients and fractals, International Journal of Innovative Computing, Information and Control, vol.2, no.6, pp.1281-1299, 2006.
23. C. Thiel, Multiple Classifier Fusion Incorporating Certainty Factors, Mastre Thesis, Ulm University, Germany, 2004.
24. J. J. Lu, Construction and Testing of A Mandarin Emotional Speech Database and Its Application, M.S. thesis, Tatung University, Taipei, Taiwan, 2004.
25. C. Burges, A tutorial on support vector machines for pattern recognition, Data Mining and Knowledge Discovery, vol.2, no.2, pp.955-974, 1998.
26. T. L. Pao, Y. T. Chen, J. H. Yeh, Y. M. Cheng and Y. Y. Lin, A comparative study of different weighting schemes on KNN based emotion recognition in Mandarin speech, Proc. of the International Conference on Intelligent Computing, China, pp.997-1005, 2007.
27. Y. Roggo, L. Duponchel and J. P. Huvenne, Comparison of supervised pattern recognition methods with McNemar's statistical test-application to qualitative analysis of sugar beet by near-infrared spectroscopy, Analytica Chimica Acta, vol.477, pp.187-200, 2003.
28. K. Horio and T. Yamakawa, Handwritten character recognition based on relative position of local features extracted by self-organizing maps, International Journal of Innovative Computing, Information and Control, vol.3, no.4, pp.789-798, 2007.